Don’t Play it Safe
If your team’s strategy is predominantly based on ‘historical look backs,’ or if it is a mere incremental approach to growth, you may fall short. This approach, typically, only allows you to play safe, along with the rest of your industry counterparts.
Additionally, besides the usual pitfalls, human dynamics may influence the outcomes of your strategic plans. Primarily because people often extrapolate from their own experiences and data, even when they are trying to do something new. Consequently, growth strategies are often contaminated by overconfidence and other cognitive biases, and to some extent, internal politics as well.
The Market is Constantly & Rapidly Changing
The digital environment requires immense imagination and skill because moves at an incredible pace. Customer preferences are constantly chaging and brand loyalty keeps eroding, as the competition is just one click apart. Therefore, in this volatile environment, traditional growth planning methods can only move revenue needles by so much.
Indeed, this situation can be compared to the evolution of performance management workflows. Performance management changed from Peter Drucker’s Management by Objectives (MBO), to Robert Kaplan’s Balance Scorecards, and then to Andy Groove’s Objectives & Key Results (OKRs). Companies should adopt a similar approach to growth planning to ensure that it keeps up with the times.
Aim Big to Achieve Big – Not Just Growth but 2X Growth
Most often, teams start their planning with data that they already have and look for winning nuggets within that data to identify where it can be applied. This approach is good—however, it has limited impact, as you may not be able envision new opportunities.
To gain unprecedented big wins, companies need to strategize with the end in mind. Managers can kick off this process by asking team members to identify products they would sell and markets they would enter, if they had no budgetary restrictions. Their inputs can then be used to form the basis of the company’s growth strategy, because most often than not, your team would have identified whitespaces in your market.
With these whitespaces in mind, your growth team can reverse engineer conversion data and look for winning cohorts to invest in and accelerate growth. These cohorts can be based three main variables customer acquisition (conversion rate), average order value and retention, which are arguably, the three most important growth metrics. Even a slight improvement in one of those variables can give you a significant uplift. Why? Because these variables multiply and compound. A mere 30% increase in each of the three variables can more than double a company’s revenue, 1.3 x 1.3 x 1.3 = 220%.
The Right Tech Makes It Possible
However, creating and identifying winning cohorts manually is challenging. It is virtually impossible given the millions of data points today’s web platforms capture. Most companies don’t know how to use all the data that have to make informed growth strategies. This shortcoming is requires immediate attention if companies want to remain relevant. An easy solution would be to invest in an automated AI data science platform like Alavi.ai. This platform uses artificial intelligence (AI) to identify cohorts that are bound to outperform current base lines built on pure historical lookbacks. It’s AI is created to help companies scale at large without out impacting acquisition costs or ROI. Alavi makes sure companies are make use of all their data to grow and scale in a variety of ways.
Sustainable Process = Sustainable Growth
The key to making your growth planning effective is to have a sustainable process that assists your planning cycles in both the short and long term. A platform like Alavi helps you continuously assess changing customer dynamics, monitor impact and identify opportunities to modify course in this unpredictable world.
Alavi helps ensures that you are constantly moving and not just standing still. The key to winning is to create an organization that identifies winning sprints backed by data science and works backwards with a clear end game in mind.